“If every employee hits their goals, will your organization execute its strategy?”

That was the key question in today’s webinar by Donald Sull from MIT’s Sloan School of Management. It’s an incredible insight, as collectively we’ve all embraced goal-setting in the 100 years or so since General Motors popularized the idea.

But does it work? Is there enough transparency corporately to ensure that each contributors goals are in line with the overall strategy, and that if all the individual goals are met that the executive strategy would actually be executed?

AI is fascinating, but most organizations don’t have the magnitude of data or the skills to effectively leverage it.

Watching Qlik’s Qonnections 2018 keynote yesterday opened my eyes to a new application of ai. Qlik claimed that Qonnections was the largest data analytics conference in the world. Not sure about that one, but their existing 45,000 customers is hard to argue. Their CEO make some interesting claims, that 10 of the 10 largest pharma companies use Qlik, along with an impressive list of the top 10 companies across several leading industries.

The progression is always like this: data -> analytics -> insights. I’ve heard it many ways from many suppliers, but the two biggest hurdles with data are always the same. Gathering high-quality data is difficult. There are lots of tools for display once clean data is available. Regardless of the quality of the tools, deriving actionable insights is tricky.

It all starts with data literacy and a data-centric culture.

Qlik’s director of research presented their soon to be released “Cognitive Engine”. The highlight of the cognitive engine is their clever use of AI. With Qlik’s new technology, a researcher can visually drill into apparent anomalies in the data, and Cognitive Engine’s AI will proactively identify statistically significant variances worth exploring.

This, to me, was fascinating. It was clearly one of the quickest and easiest ways a data-centric organization could start to leverage AI.

One of the highlights of attending the GPU Technology Conference in San Jose back in March was the keynote address by NVIDIA CEO Jensen Huang. Of course, there was the predictable push to sell more hardware, but notwithstanding the technology demonstrations were truly impressive.

There was an autonomous car in the parking lot. There was a driver in the conference room. Then, on the big screen, was the driver in the holodeck – the simulated environment for the real car out back. The real driver, in the virtual holodeck, seeing real-time data and imagery, drove the virtual car, which in turn drove the physical car safely into a parking spot. It was impressive. The processing power required to do this all real-time is barely imaginable.

But the biggest insight for me came from NVIDIA’s Director of Developer Programs, William Ramey.

AI is not Magic.

With all the mystery around Machine Learning and Deep Learning in particular, it was insightful to hear such a thing. The problem, of course, is that the hype is so great. It’s easy to assume that because this problem is hard, AI must be the answer.

His statement boiled down to this: If X then Y. Can the problem be defined so simply? Can a human understand, at least conceptually, the process? Is there a defined outcome? If all are true, then AI is likely a viable candidate. If a human can’t articulate the process, then don’t expect AI to provide an answer.

Ultimately it boils down to this: Could a human, with a lot of data and a lot of time, solve this same problem? If not, don’t expect magic from AI.

Why is the real world made up of exchange networks rather than markets? In a word: trust.

Relationships in an exchange network quickly become stable (we go back again and again to the person who gives us the best deal), and with stability comes trust, i.e., the expectation of a continued valuable relationship. This is different than in a typical market, where a buyer may deal with a different seller every day as prices fluctuate. In exchange networks, buyers and sellers can more easily build up the trust that makes society resilient in times of great stress. In markets, one must usually rely on having access to an accurate reputation mechanism that rates all the participants, or to an outside referee to enforce the rules.

Amazon’s webinar today highlighted several ways that Big Data is being used in the HCLS industry. One of the key insights was the self-realization within one of the featured medical practices that they were “data rich, insight poor.” Having tremendous amounts of data isn’t much value to anyone without the appropriate tools and skills to visualize, interact and explore the data, which of course needs to be coupled with people who are empowered to act on the insights revealed.

Several of the tools demonstrated seemed to use Shiny and R to create interactive graphics. This is a perfect combination of developer skills preparing the right kind of visualizations so that self-service data exploration becomes meaningful. So many self-service reporting tools end up being huge time sinks because the fundamental data organization was incomplete or inconsistent.

One of the featured labs highlighted how expenses were reduced and customer satisfaction increased by the appropriate use of dashboards and educated data exploration. They were able to reduce repetitive testing and quickly identify trends in unreimbursed testing. Then with data at their fingertips they were able to discuss the trade-offs between economic and medical goals.

Day Trading 101 is a great introduction into the world of financial trading.

Trading requires a different mindset—one of getting in and getting out at the right times instead of holding until fundamentals change. Since the goal is so different than investing the rules are different. The book makes it very clear that the number one rule in trading is capital preservation.

The book is designed for people without any investing knowledge but is certainly easier to read if you already understand a bit about investing. There are chapters about interdependencies of currency markets, reading candlestick charts, derivatives and futures, commodities, an explanation of support and resistance, and building trade pyramids to minimize risk. All of these topics are approached in a light and easy to comprehend style, though if you really want to get into trading you’ll likely have to do additional reading to begin.

My interest in day trading is really about trading cryptocurrencies based on data, and this book makes no mention of the cryptocurrency market. However, the insights about capital preservation have been critical in our new trading bot. The safety-oriented model built into the bot largely avoided last week’s carnage in the Bitcoin market. ($11,600 on March 5, 2018 to $7331 on 3/18).

If you have any interest in trading and speculation over investing, you should definitely give Day Trading 101 a quick read.